# 33rd place solution - LB 0.758

TL;DR
We used a conformer-like model consisting transformer encoder, 1d convolution with CBAM and bi-LSTM.
Overall model size is about 16MB after INT8 quantization.
Training objective is CTC with InterCTC loss.
Data preprocess
Used landmarks
1 for nose, 21 for dominant hand, 40 for lips
x, y coordinates
Normalization
Standardized distance from nose coordinates
Feature engineering
Concatenation of normalized location, difference of next frame, joint distance of hand
Total 582 dims
Removing data that input frame is shorter than 2 times of target phrase
Data augmentation
Horizontal flip landmarks
Interpolation
Affine transfrom
Model
2 stacked encoder with Transformer, 1D convolution with CBAM and Bi-LSTM
hidden dim: 352
CTC loss and Inter CTC loss after first encoder
17M parameters and INT8 quantization
Train
2 staged training (300 + 200 epochs)
Use supplemental and train data in first 300 epochs
Use train data 200 epochs
Ranger optimizer
Cosine decay scheduler with 12 epochs warmup
AWP
It prevents the validation loss from diverging, but it doesn't seem to improve the edit distance.
Used to train long epochs.
Not worked
Augmentation
Time, spatial, landmark masking
Time reverse
Autoregressive decoder
Use joint loss, CTC loss for encoder and Crossentropy for decoder
Inference time is longer and the number of parameters larger than those of the CTC encoder alone, but the performance improvement is not clear, so it is not used.